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RESEARCH REPORTS

2001-2002

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2 Laboratory for Manufacturing Automation

RESEARCH REPORTS2001-2002

edited by

Prof. David A. Dornfeld

Miguel C. Avila

April 2002

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Research Reports 20012002 3

CONTENTS

PREFACE ....6

BIO: PROF. DAVID A. DORNFELD....9

LMA ON THE WORLD WIDE WEB..10

MEMBERS OF THE LMA RESEARCH GROUP, 2001-2002...11

BURR RESEARCH GROUP......14

CONSORTIUM ON DEBURRING AND EDGE FINISHING (CODEF)...15

Comprehensive approach to Burr Prediction

Amit Bansal...18

Study of Burr Size and Surface Roughness in High Speed Face Milling

Amit Bansal and Kiha Lee25

Tool Path Planning in conjunction with Exit Order SequenceAmit Bansal and Kiha Lee30

Influence of Work Hardening Effect on the Exit Burr Formation in 2 Consecutive

Ortogonal CuttingsJihong Choi36

Optimization and Quality Control in Burr Formation using Design of Experiment (I):

Face-MillingKiha Lee.39

Optimization and Quality Control in Burr Formation using Design of Experiment (II):

Drilling of Intersecting HolesKiha Lee45

A Study of Surface Roughness in Micro-End-Milling of AluminumKiha Lee48

Integrated Tool Path Planning and Burr Prediction System for Face MillingPrabhu Ramachandran and Amit Bansal..53

Probabilistic based Precision Process Planning P4Arvind Rangarajan57

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Optimization of the Face Milling Process: Tool Path and Tool Design Issues, Part 1.

Algorithm and Tool Path Design

Arvind Rangarajan.61

Report on Sandvik Case StudyArvind Rangarajan.69

A Finite Element Model of Face Milling

Borlin Shyu74

Development of a Milling-Burr Control ChartChristian Rosier.79

PRECISION RESEARCH GROUP 80

End-point detection in the CMP process: review of current approaches

Edward I. Hwang...81

End-point detection in the CMP process: AE Sensor

Edward I. Hwang ..88

A Hierarchical Framework for Integration of Fundamental Physical-Chemical Modelingto inform Environmental Decision Making:

A Case study using the Environmental Value Systems Analysis

Nikhil Krishnan and Uday Ayyagari 95

SMART Pad Fabrication for CMP

Sunghoon Lee and Edward Hwang 100

Material Removal Regions in Chemical Mechanical Polishing: Coupling Effects of SlurryChemicals, Abrasive Size Distribution and Wafer-Pad Contact Area, Part 1

Jianfeng Luo 105

Material Removal Regions in Chemical Mechanical Polishing: Coupling Effects of SlurryChemicals, Abrasive Size Distribution and Wafer-Pad Contact Area, Part 2

Jianfeng Luo...126

A Macroscopic Model for CMP

Zhoujie Mao 138

ARCHIVAL JOURNAL PUBLICATIONS141

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REFEREED CONFERENCE PROCEEDINGS 151

NON-REFEREED PUBLICATIONS-INVITED AND CONFERENCE PAPERSREPORTS/EDITED158

VOLUMES/CHAPTERS IN BOOKS 163

FURTHER INFORMATION..166

RESEARCH SPONSORS...166

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PREFACE

Background The research work in the Laboratory for Manufacturing Automation(LMA) in the Mechanical Engineering Department (ME) (and affiliated with theEngineering Systems Research Center) of the University of California at Berkeley ismotivated by the desire to reduce the uncertainty in the operation of manufacturingprocess so that technologically sophisticated products or components can bemanufactured successfully and competitively. Many recent studies have analyzed thevarious aspects of manufacturing processes, machinery and systems and concluded that,for increased productivity, strong moves towards increased flexibility, precision,integration and computer control are needed. Unfortunately, a production line which ishighly automated is not necessarily also highly flexible or programmable. Most existingautomated machines, such as computer numerically controlled (CNC) machine tools androbotic-based manufacturing and assembly lines require a large effort in task-planning,program development and debugging and control tuning at the time of their installationand production start-up. This problem is compounded by relatively poor adaptability ofthese machines to changes in the production lines or processes, task scheduling andproduct modifications since most machines are designed to perform repetitive tasks for

high production volumes and are not equipped with sufficient electronic or mechanicalflexibility to detect changing conditions to adapt accordingly. If we add to the desire forincreased flexibility a requirement for manufacturing parts and products that maintaintight dimensional and form tolerances, i.e., precision manufacturing, the need to adapt tovariations in machine performance and tool and workpiece variation is even more critical.

The inability of these systems to respond to changing conditions and the requirement formore precision in manufacturing are driving much of the research at Berkeley in theLaboratory for Manufacturing Automation. The overall goal of this research program isto reduce the uncertainty in the performance of manufacturing processes and systems.This uncertainty in operation is the source of most errors and in-process failures and isone of the key challenges to successful operation to meet the demands of productiontoday and in the future. Of increasing importance is the development of tools for

designers to use so that they may assess the impacts of the manufacturing process on thedesign and performance of their products and assemblies. In fact, the successful researchprogram will, at one level, develop predictive process models for control andoptimization of the process and, at a much higher level, wrap these models in software sothat they are easily and effectively utilized by designers. The LMA is collaboratingextensively with industry (through CODEF, for example) and with Professor Paul Wright(also in Mechanical Engineering at Berkeley) in a National Science Foundation fundedproject on Rapid Prototyping that addresses all aspects of the design to fabrication ofprecision products.

Research Overview The research in the LMA is split into two basic areas reflecting theprojects and interests of the sponsors- precision manufacturing and burr modeling,deburring and edge finishing. Precision manufacturing research has been growing

steadily due to strong support from the computer peripheral industry (read-write headsand high density storage devices), the semiconductor "chip" manufacturers due to anincreasing need for "planarization" technology in the fabrication of multi-layer devicesand MEMS structures, and the precision component industry (aerospace, disk dives, fuelinjectors). The second area is represented by an industry consortium, CODEF, describedin more detail later in the report. A third, area of interest is pursued in collaboration withProfessor Paul Wright in the Mechanical Engineering Department at Berkeley, on rapidprototyping of mechanical products and internet-based manufacturing - CYBERCUT.Finally, Professor Fiona Doyle of MSME at Berkeley has been contributing to an

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understanding the chemical issues in polishing and CMP. Since the departure ofProfessor Paul Sheng., the LMA is increasing its activities in the are of environmentallyconscious manufacturing, with special emphasis on the semiconductor industry andelectronics manufacturing.

At the University of California at Berkeley, engineering faculty have been working for a

long time in a number of areas vital to manufacturing technology. Research at Berkeley isstrongly motivated by modern automation as described above. Research issues listedbelow are all fundamental to both flexible automation and computer integratedmanufacturing:

process planning, including maximum utilization of manufacturing tools andprocesses for increased productivity, e.g. minimum downtime for maintenance

automated monitoring and failure detection

analysis and development of processes with minimal environmental impact

intelligent sensors (processing of multiple sensor outputs for feature extraction,decision making, and collection of information from processes)

ability to adapt to variability in materials and processing conditions

burr formation modeling and deburring

database, knowledge base and expert systems for design and manufacturingautomation

integration of new process technology into existing systems at minimum cost

precision manufacturing

This research report gives a review of the projects in the laboratory under the supervisionof Professor David Dornfeld. The projects include development of sensing methodologies

and signal processing for process monitoring and analysis as well as advanced techniquesfor intelligent sensor-based process control. In addition, basic studies on processing

leading to the development of models for component design and process control areincluded - specially for precision manufacturing. A key aspect of these research projects

is that the results must be able to be demonstrated and evaluated in the laboratory. Thereis a strong industrial interaction as part of most of the

research projects.

Facilities for Research The research undertaken by the faculty and students in theLMA necessitates first rate equipment and laboratory facilities. In this environment,faculty, visiting researchers and graduate researchers can develop, implement and testindividual ideas and cooperative research focusing on the control of machines, sensingand signal processing and machine-to-machine communications. The LMA is equippedwith Matsuura 510V machining center, Mori Seiki NC Drilling machine, Pneumo

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Diamond Turning Lathe and Kugler Ultra Precision Milling machine. In addition, aninstrumented laboratory scale polishing/CMP machine and a modified polishing tableequipped with fluid coupled AE sensors and a z-axis force dynamometer for tape/slurrytexturing and burnishing is available. A variety of conventional machine tools are alsoavailable for research (cylindrical and surface grinders, for example). Instrumentation and

computers for advanced signal acquisition and processing are available including an arrayof computers for data collection and analysis (PCs, RISC 6000 workstations) terminalson the Ethernet, MAC, Hewlett-Packard high speed data acquisition system and a varietyof sensors (force/torque, current, laser profilometer and acoustic emission) and highspeed AD/DA interface and digital data sampling hardware. Metrology hardware forsurface characterization (Zygo and Talysurf 10 with computer interface) and scanningelectron microscope with powered in-situ machining stage, HP laser interferometer areavailable. A full scale metallography laboratory for polishing and macro-examination ofspecimens is available. The LMA has joint projects with the Micrfolab in EECS and hasaccess to those facilities as well. We also have a micromachining center coordinated withProfessor H. Kazeroonis lab at the Richmond Field Station. A Consortium on Deburring

and Edge Finishing (CODEF) has been established within the LMA to investigate burrformation mechanisms in various machining operations, to develop design guidelines tominimize the occurrence of burrs, and to develop advanced automated burr detection anddeburring techniques. This consortium is described further in the following pages of thissection. A consortium to support precision manufacturing research is under developmentwith strong emphasis on semiconductor manufacturing/chemical polishing.

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David A. Dornfeld

David Dornfeld received his B.S., M.S. and Ph.D. degrees in Mechanical Engineeringfrom the University of Wisconsin-Madison in 1976 in the area of ProductionEngineering. His Ph.D. thesis concerned the study of the fundamentals of the mechanical

pulping process (abrasive machining). He joined the faculty of the University ofCalifornia at Berkeley in the Mechanical Engineering Department in 1977 and ispresently Professor of Manufacturing Engineering. Since July 1, 1999 he holds the firstWill C. Hall Family Chair in Engineering. He is past-Director of the EngineeringSystems Research Center in the College of Engineering. In 1982 and 1992 he wasDirecteur de Recherche Associe, Ecole Nationale Superieure des Mines de Paris, Parisand Invited Professor, Ecole Nationale Superieure d'Arts et Metiers- ENSAM, Paris,respectively.

Dr. Dornfeld's research activities are in several fields of manufacturing engineering andflexible automation: acoustic emission monitoring and analysis of manufacturing

processes; burr formation and edge finishing (leads an industry consortium supportingwork in this area); precision manufacturing; green manufacturing; and intelligent sensors

and signal processing for process monitoring and optimization. He has published over200 papers in these fields, contributed chapters to several books and has four patents

based on his research work. He is a consultant on sensors, manufacturing productivityand automation and process modeling and the associated intellectual property issues.

Professor Dornfeld is a Fellow and an active member of the American Society ofMechanical Engineers (ASME), contributing to the technical programs and journals of

the society. He is the past Technical Editor, Trans. ASME, Journal of Engineering forIndustry. He was the recipient of the ASME Blackall Machine Tool and Gage Award in

1986. He is a Fellow and past-Director of the Society of Manufacturing Engineers

(SME), member of Japan Society of Precision Engineering (JSPE), American Society ofPrecision Engineering (ASPE) and the U.S. Acoustic Emission Working Group (AEWG).He is past-President of the Board of Directors and a member of the Scientific Committee,

North American Manufacturing Research Institute (NAMRI/SME). He is an ActiveMember of the CIRP (International Institution for Production Engineering Research)

where he served as co-chair of the Working Group on Tool Condition Monitoring and isChair of the Scientific Technical Committee on Cutting.

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Visit the LMA on the World Wide Web

http://LMA.Berkeley.edu

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MEMBERS OF THE LMA RESEARCH GROUP, 2001-2002

Postdoctoral Researchers

Graduate Students

Name Academic Background Field of Interest

Sangkee MinB.S. 1991, Yonsei University, KoreaM.S. 1993, Yonsei University, KoreaPh.D. 2002, UC Berkeley

FEM, Burr minimization,Maufacturing.

Miguel AvilaDiploma in M.E. 2000, Central University ofVenezuela, Caracas.

Manufacturing Technology.

UdayakiranAyyagari

B. S. 2001, IIT, Madras, India.Green Manufacturing and

Design, Semiconductor

Manufacturing Processes.

Amit Bansal B. Tech. 2000, IIT, Madras, India. Manufacturing Technology.

Peter BroomesB.E. in Mechaincal Engineering

Stevens Institute of Technology

Green Manufacturing, ProcessModeling.

Andrew ChangB. S. 1995, UC BerkeleyM.S. 1997, UC Berkeley

CMP, Precision Manufacturing.

Jihong Choi B.S. 2001 Yonsei University, Korea Burr minimization, FEA.

Edward HwangB. S. 1997, Seoul Natl Univ. KoreaM. S. 1999, Seoul Natl Univ. Korea

CMP, MEMS, PrecisionManufacturing

Christian Keppeler B. S. 2000 Georgia Institute of Technology CMP pad design.

Nikhil Krishnan B. Tech 1997, IIT, Madras, IndiaM.S. 1999 UC Berkeley CMP, Green Manufacturing.

Kiha LeeB.S. 1997, Yonsei University, KoreaM.S. 1999, Seoul Natl Univ. Korea

Burr Formation in Micro-Machining.

Sunghoon LeeB. S. Pusan National University, Korea

M. S. Pusan National University, KoreaCMP, IC fabrication.

Jianfeng LuoB. S. 1997, Tsinghua University, ChinaM. S. 1998, University of Cincinnati


Zhoujie MaoB. S. 1992, Dalian Univ. of Tech, ChinaM. S. 1995, Tsinghua University, China


Eric Masanet

B.S. 1994 University of Wisconsin

M.S.1996 Northwestern University

Green Manufacturing and

Design, Electronics Industry.

Arvind RangarajanB. Tech.1999, IIT, Madras, IndiaM.S. 2001, UC Berkeley

CAD/CAM.

PrabhuRamachandran

B. Tech 2001, IIT, Madras, India CAD/CAM.

Joaquin RosalesB.S. 2001, nstituto Tecnologico y de Estudios

Superiores de Monterrey, Monterrey, Mexico

Green Manufacturing, Thermal

Finite Element Methods.

Bor-Lin ShyuB.S. 1987 Natl Taiwan Univ. TaiwanM.S. 1991, Natl Taiwan Univ. Taiwan

FEM, Manufacturing.

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Visiting Scholars

Arnaud De GraveAgregation in M.E. 1999, ParisMasters Degree Ind. Eng. 2000, Grenoble

Concurrent Engineering,

Integrated Design,

Micro-Machining, MEMS

Carlos Magno deOliveira

B.S 1996, University of Sao Paulo, BrazilM.S. 1999, University of Sao Paulo, Brazil

Machining processes, Monitoring

systems, Data communication,

Robotics, Artificial Intelligence.

Holger HeupsDiploma in M.E. 2002, Univ.of Tech.Aachen, Germany

Manufacturing Technology.

Haedo JeongB.S 1987 Pusan National University, Korea

M.S 1989 KAIST

Ph.D 1994 University of Tokyo

Grinding, CMP, Micro Machining,

Rapid Prototyping.

Paul Junkermann Vordiplom 1998 RWTH Aachen FEA of SPDT.

Christian RosierDiploma in M.E. 2002, Univ.of Tech.

Aachen, GermanyManufacturing Technology.

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Members of the LMA Research Group, 2001-2002

Back row: Kiha Lee, Christian Keppeler, Prof. David Dornfeld, Jianfeng Luo, PeterBroomes, Zhoujie Mao, Udayakiran Ayyagari, Prahbu Ramachandran.

Middle row: Nikhil Krishnan, Andrew Chang, Miguel Avila, Bor-Lin Shyu, JoaquinRosales, Sunghoon Lee.

Front row:Jihong Choi, Christian Rosier, Sangkee Min, Arvind Rangarajan, EdwardHwang.

Missing in photo: Amit Bansal, Haedo Jeong, Holger Heups, Arnaud De Grave, PaulJunkermann, Calos Magno de Oliveira, Eric Masanet.

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BURR RESEARCH GROUP

Sponsored by

CODEF Members of Researchers

Miguel Avila

Amit Bansal

Jihong Choi

Holger Heups

Kiha LeeSangkee Min

Prahbu Ramachandran

Arvind Rangarajan

Christian Rosier

Borlin Shyu

List of CODEF Sponsors

Boeing

Caterpillar, Inc.

Daimler-Chrysler GmbH

Ford Motor Company

Hibbit, Karlsson & Sorensen, Inc.National Science Foundation

AB Sandvik Coromat

Industrial Affiliates of the LMA

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CONSORTIUM ON DEBURRING AND EDGE FINISHING (CODEF)

Professor David Dornfeld, Director

Laboratory for Manufacturing Automation5100A Etcheverry Hall

University of CaliforniaBerkeley, CA 94720-1740 USA

(510) 642-0906Fax: (510) 643-7463

E-mail: [email protected]

BACKGROUND

The CONSORTIUM ON DEBURRING AND EDGE FINISHING (CODEF) wasestablished to address problems related to prediction and modeling of burr and edgebreakout phenomena, development of a data base (or knowledge base) of best burravoidance and removal techniques from industry, development of a computer aideddesign (CAD) advisor (a burr expert) for designers and process planners, developmentof strategies for deburring cell level process feedback and control and identification anddevelopment of advanced deburring technologies. Although all manufacturingprocesses are of interest in this program, of special interest is those processes forprecision manufacturing. The program also provides a forum for the discussion ofstandards and terminology associated with the field.

The CODEF is designed to be "industry driven" in the sense that the participation ofindustry in the identification of specific research objectives and applications forevaluation of results of research is aggressively sought. Industry sponsorship of theCODEF is also sought although there are opportunities for federal agency funding of

basic research in this area (NSF University Industry Cooperative Research program, forexample) or focused application research (NSF/ARPA Machine Tool AgileManufacturing Research Institute at Illinois, for example). However, the quality andusefulness of the research and the potential for program support are dependent on theability to link effectively with industry. The products of the consortium, in addition to theforum for discussion of challenges in this area, are:

software (CAD burr expert, for example)

database of burr minimization and deburring knowledge

burr formation models

applications oriented solutions to deburring and edge finishing problems deburring hardware/strategy evaluation

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engineers trained in burr minimization and deburring techniques

There may be additional products of the consortium as it develops in response to specificrequests and interests of the members. The basic objective of the CODEF is to provide achannel for the transition of basic research on burr/breakout formation modeling and

deburring techniques into software and systems for industry to use to solve realmanufacturing problems. To accomplish this objective the consortium activities must be

strongly linked to industry which means the active participation of industry.

CONSORTIUM PARTICIPATION

To accomplish this linkage (in both directions- industry advisors for the consortium and

research results and technology introduced in industry) membership will be solicited inthe consortium according to the following format:

Membership levels are established at two levels, Observer and Member, as

follows:

Observer- this level of participation allows the company to send arepresentative to the annual review meeting of the CODEF, receive pre-publication copies of all CODEF research reports, and priority access tofaculty student researchers in the LMA; a fee of $15,000/year with athree year commitment (preferred) is required for participation at theObserver level.

Member- this level of participation includes the benefits of the Observermembership level plus participation in the CODEF advisory board(determining research priorities and directions for software andhardware development), the right to provide a "prototype part" forevaluation of developing burr minimization and deburring strategies,access to software for evaluation in your facility, assistance in licensingsoftware from the University of California for use, visit to yourcompany by CODEF researcher, opportunity for industry researcher tovisit LMA for several days; a fee of $30,000/year with a three yearcommitment (preferred) is required for participation at the Memberlevel.

(Note: The fees for these two levels of participation are paid as gifts and, thus,incur no overhead charges.)

Additional, specific, research projects can be arranged in the CODEF beyond the

two levels of participation outlined above. Industrial Research Fellows from CODEF member companies can spend

extended periods of time (for example, one year) on site working on a specificresearch project and participating in the activities of CODEF; there is anadditional fee of $35,000 per year to cover the cost of hosting the individual-office space, copying, telephone, facsimile machine, etc.

Advisory Board- CODEF will have an informal advisory board comprised of

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Member-level participants, representatives of faculty researchers, and, asappropriate, selected outside members.

Short courses and special seminars on burr minimization, deburring or othertopics are also offered.

Free access to burrExpert.com website for information, problem-solving andresource referral

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Comprehensive approach to Burr Prediction

Amit BansalSponsored by CODEF

Abstract The focus of this research is to modify and expand the currentalgorithm for Exit Order Sequence (EOS) scheme, a very reliable burrprediction tool for face milling operation. Given the complexity of thecalculations involved and lack of analytical solutions, the new algorithmtakes a more general approach by tessellating the curved edges into smallstraight edges. With this approach the algorithm is applicable to any givenpart geometry and at the same time to any given tool-path (NC or CNC)for that part geometry. A fully interactive graphical user interface (GUI)has been implemented with built-in solid geometric viewer. A burr size

database has also been developed, which quantifies the burr size based onthe EOS.

Keywords: burr prediction, exit order sequence, face milling, graphic userinterface.

1. Exit Order Sequence [1]

During the face milling process, orientation of the material being pushed out or broken

(depending on ductility of the material) is determined by feed direction, insert geometryand workpiece edge orientation. Order in which the three points A, B and C on the toolinsert come out of the workpiece edge determines the burr size (Figures 1(a) and 1(b)). Inother words orientation of cutting edge plays an important role in burr formation whilethe tool exits the workpiece edge.

insert

major cutting

edge

minor cuttingedge

A B

C

insert

major cutting

edge

minor cuttingedge

A B

C

Machined surfaceinsert

major cutting

edge

minor cuttingedge

A B

C

insert

major cutting

edge

minor cuttingedge

A B

C

insert

major cutting

edge

minor cuttingedge

A B

C

insert

major cutting

edge

minor cuttingedge

A B

C

insert

major cutting

edge

minor cuttingedge

A B

C

Machined surface

Figure 1(a). Points A, B and C on tool insert.

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Figure 1(b). Exit Order Sequence

Calculation of location of points A, B and C on the insert with respect to tool centerrequires axial rake angle (a), radial rake angle (b), tool lead angle (g), feed per tooth (F),face cutting edge angle (F) and depth of cut (d) (Figure 2).

OB= ( r.cosu, r.sinu, 0)

OA= (r.cosu- |F.cosu|.cosu+ |F.cosu|.sinu.tanb, r.sinu- |F.cosu|.sinu-

F.cosu|.cosu.tanb, |F.cosu|.tanF)

OC= (r.cosu- d.tana.sinu+ d.cotg.cosu, r.sinu+ d.cotg.sinu+ d.tana.cosu, d)

Figure 2. Geometric location of points A, B and C.

2. Tessellation

As shown above, the equations for determining the points A, B and C are very complex

and given curves and splines present in the tool path and work piece geometry, thecalculation becomes so much complicated that an analytical solution is not feasible.Previous work by Kumar[1] is limited to straight lines for both part geometry and toolpath. Current algorithm uses tessellation to solve this problem and is applicable to anytype of curve geometry.

A

B

C

B

B

C

CAB

AA

B

ABC

A

B

C

BAC ACB

A

B

C

A

B

C

BCA

A

B

C

CBA

Burr Size increases

Z

Y

XuEY

EX

A

C

B

F

a

g

EZ

o O A BC

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In the process of tessellation, any geometry (an edge in this case) is discretized into smallgeometric entities. The precision of tessellation can be set depending on the partcomplexity. Coordinates of all the newly created edges are stored and new straight lineedges are created joining them in that order to get new loops. These loops are furtherswept to 3D bodies and the bodies belonging to inner loops are subtracted from the body

created for big outer loop to get the final tessellated body (Figure3). A similar attempt ismade for the tool path also. Flow chart for tessellation process is shown in Figure 4.

EOS prediction scheme is then applied to this newly created body for the newly createdtool path. Figure 5 shows a comparison between the results produced with and with outtessellation. It is clear that the prediction system produces very arbitrary results forcurved edges with out tessellation.

Figure 3. Part before and after tessellation (precision of tessellation has been decreasedfor clarity).

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Figure 4. Flow chart for tessellation algorithm.

Input Part/ Tool Path

Break in to St. Edges

Store Edges in a Linked List

Check for curved edges in theloop

Store Loops in a Linked List

Sweep Loop to Body

Subtract inner bodies fromOuter Body

If NOT Endof Loop List

Tessellated Body

Tessellated Tool Path

Yes

No

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Figure 5. EOS algorithm applied to the part before and after tessellation

3. Graphic User Interface (GUI)

A fully interactive GUI has been developed with an inbuilt solid geometric viewer. ThisGUI has been developed for the Windows operating system for its wide usage and userfriendliness. Microsoft Visual C++ is used as the development tool with ACIS solidgeometric modeler 6.3. This interface provides user to open a particular part file in *.satformat. A tool path parser is also provided which facilitates direct input of the tool path inNC/CNC format with minimum user modifications. An input panel feeds all initialgeometric and cutting parameters e.g. various angles, feed, tool radius etc to the softwareas shown in Figure 6.

The software executes the EOS algorithm and saves the tessellated body and the finalresults showing various EOS on the part geometry along with tool path in the currentdirectory in *.sat format and opens the latter in the viewer. A snapshot of the softwareapplication has been shown in Figure 7. An error log is also created informing the user

ABC BAC ACB BCA CAB CBA

Tool Path

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about various errors occurred during the execution of the algorithm so that user can makeappropriate modifications, mainly to the tool path.

Apart from executing the EOS algorithm the software provide all the necessary featuresof a solid geometric modeler viz.zooming, panning, various views etc.

Figure 6. Dialog box to input parameters.

Figure 7. A snapshot of the software GUI, showing a part and resultant EOS.

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4. Quantification of burr size

A burr size database has been created based on the various experiments done at Daimler

Chrysler AG, Germany. These experiments were performed on four different materials

AlSi7Mg, AlSi10Mg and Mg(AS21), Mg(AS41) in order to understand the effect of

material properties on exit order sequence. These materials were tested for various

combinations of tool geometries (rake and lead angles) and cutting parameters (speed,

feed rate and depth of cut). The results matched with respective theoretical values to an

extent. These experiments also bolstered the fact that material ductility is the most

important material property affecting burr size [1].

The user selects the work piece material from a pull down menu. Currently efforts arebeing made to include more materials into the database and also find some burr sizecorrelation between various alloys of a materials, which have more than one constitutingelements in common. In case the user does not find the material he or she is looking for, a

help file is provided which carries information on various material properties. The user isencouraged to choose a material which matches the one used most often.

The database is created in Microsoft Access and is connected to the software throughdatabase integration tools provided by Microsoft Foundation Classes (MFC). Thematerial information with various EOS present in the work piece is forwarded to thedatabase and appropriate information is retrieved and displayed to the user. The databaseprovides a mean value as well as a burr size interval where the value is most likely to fall.Building of data display is still under development at this time

References

[1] Kumar, S., Basic Model for Burr Prediction System, Masters Dissertation 2001,

UC Berkeley

[2] Narayanaswami, R. and Dornfeld, D.A., Burr Minimization in face milling a

geometric approach, ASME Journal Of Manufacturing Science and Enginnering,

Vol.119, May 1997, pp.170-177.

[3] Gillespie, L.K. and Blotter, P.T., The Formation and Properties of Machining

Burr,ASME Journal of Engineering for Industry, Vol. 98, No. 1, Feb. 1976, pp.

66-74.

[4] Hashimura, M. and Dornfeld, D.A., Analysis of burr formation mechanism in

machining process, 1st

International Machining and Grinding Conference, Sep.

1995, Dearborn, Michigan, SME.

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Study of Burr Size and Surface Roughness in High Speed Face Milling

Amit Bansal and Kiha LeeSponsored by CODEF and NSF

Abstract Due to rapid growth and competitiveness of the automobileindustry, reduction in total machining time has become a basic necessity

and many industries are switching to high speed machining. This studypresents the results of tests done with a high speed face-milling machine

tool at Daimler-Chrysler, Germany. Experiments were performed on fourdifferent materials for various combinations of tool geometries (rake and

lead angles) and cutting parameters (speed, feed rate and depth of cut). Asubstantial amount of experimental data was collected regarding the burr

formation and surface roughness for aluminum and magnesium alloys. Asoftware tool for theoretical exit order sequence (EOS) prediction was also

developed in JAVA for verifying the experimental results.

Keywords: Aluminum, burr formation, exit order sequence, high-speedface milling, magnesium.

1. Introduction

Figure 1 shows the basic burr formation mechanism in face milling. Exit order sequence(EOS) ranks the burr size into various categories based on how three points A, B and Con tool insert exit the work piece edge. This order determines the chip flow angle at tool

exit. Six different EOSs, ABC, BAC, ACB, BCA, CAB and CBA are possible, in theorder of increasing burr size.

Figure 1. Milling burr formation mechanism.

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2. Simulation Tool for Exit Order Sequence

A software tool was developed in JAVA programming language for reference to thetheoretically predicted exit order sequence. A snapshot of this software is shown in

Figure 2. This tool provides the user with the flexibility of changing various geometricand cutting parameters to obtain resultant exit order sequence. Different tool exitscenarios have been implemented: tool exiting perpendicular to workpiece, tool movingalong the workpiece edge, etc. This tool is available as a JAVA applet on the LMAwebsite and http://www.burrExpert.com.

Figure 2. Software tool for Exit Order Sequence Prediction.

3. Experimental Setup

Four different materials AlSi7Mg, AlSi10Mg and Mg(AS21), Mg(AS41) were used forthe experiments to see the impact of material properties. Uncoated carbide inserts wereused for the experiments. Also, a single insert was used in the tool for the experiments to

prevent the influence of variation in inserts due to run-out. Figure 3 shows the tool holdercarrying four inserts and various tool angles.

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Figure 3. Tool Holder showing various tool angles.

4. Experimental Results

Figure 4 shows the results of surface roughness and burr thickness for AlSi7Mg material.Since it has been shown previously that burr height is linearly proportional to burrthickness, even though burr height was measured; only burr thickness results are shownin this report. Nine different tools and 6 combinations of cutting parameters were used asshown in Figure 4. For example, (-6, 6) represents an axial rake angle of 6and a radialrake of 6. Lead angle was fixed at 90. A cutting parameter combination (20000, 0.14,0.86) means that cutting speed is 20000 rpm, feed 0.14 mm/rev, and depth of cut0.86mm.

Figure 4. Surface roughness (left) and average burr thickness (right) for AlSi7Mg forvarious tool geometries and cutting conditions.

Based on the results it was found that high speed milling results in small burrs. Also thesurface quality at high speeds and low feed is better. A positive axial rake angle andnegative radial rake angle is a better choice for small burr size. (6,-6) is good tradeoffbetween burr size and surface quality. Tests were extended to 95lead angle and also for

Radial Rake Angle, bAxial Rake Angle, a

Lead Angle, g

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(a, -a) type tool for various values of a rake angles. These results are not shown here but(a, -a) combination in general produces small burrs. No major differences were observedbetween results for 90and 95lead angles.

Tool (-6, -6) showed very aberrant behavior. Unbelievably small burrs were noticed eventhough theoretical burr prediction gives very high burr size. However the combination

produces very bad surface quality as shown in figure 5. A possible explanation could beback cutting and rubbing of the work piece surface due to negative rake angles. Toolwear was also very high.

Figure 5. Exit condition for tool (6, -6)

Similar experiments were performed on AlSi10Mg material, which is more brittle thanAlSi7Mg. The results for surface roughness are similar to that of AlSi7Mg as shown inFigure 6. It was found that AlSi10Mg has small burr thickness for low feed and highdepth of cut while AlSi7Mg has the same for the reverse. Tool angle combination (6, -6,90/95) produces small burr in general, and AlSi10Mg especially seems to follow thistrend more closely. The burr size for AlSi10Mg is smaller than that of AlSi7Mg, becauseAlSi7Mg is slightly more ductile.

Figure 6: Comparison of Surface roughness (left) and average burr thickness (right) forAlSi7Mg and AlSi10Mg for various tool geometries and cutting conditions.

The results of surface roughness and burr thickness for two magnesium materials areshown in Figure 7. Depth of cut is important factor for burr formation in magnesium. (6,-6, 95) is best insert angle combination for getting small burr size. For ductile material,

Feed

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effect of lead angle is important. Low speed (3000 compared to 5000 rpm) producesbetter surface quality.

Figure 7: Surface roughness (left) and average burr thickness (right) for Mg(AS21) andMg(AS41) for various tool geometries and cutting conditions

5. Conclusions

It was found that inserts with positive axial rake and negative radial rake angles result ina good trade-off between small burr size and good surface quality. For small burr size and

good surface quality, high cutting speed is preferable for aluminum and low formagnesium. Exit order sequence applies appreciably for ductile material than brittle

though the comparison is not presented in this report.

6. Future Work

Currently work is underway for constructing a Milling Burr Expert System, an internet-

based expert database reference system which can be used for choosing suitable toolgeometry and cutting parameters. Also there is a need to extend the experimental work to

more tool geometries and cutting parameters for AlSi10 and magnesium materials.

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Tool Path Planning in conjunction with Exit Order Sequence

Amit Bansal and Kiha LeeSponsored by CODEF

AbstractThe purpose of this research is to bring out the advantages of

applying tool path planning in conjunction with exit order sequence (EOS)to minimize burr formation in face milling. While tool path planning

avoids exit burr condition in high priority regions, EOS can helpdetermine appropriate tool geometry and in plane exit angle for smaller

burr size in exit regions. Tests were performed on engine blocks from anautomotive production line and results were very encouraging.

Keywords: Burr formation, exit order sequence, milling, tool path.

1. Introduction

Tool exits occur only under certain circumstances for a 2D polygonal contour in planarmilling. Three distinct tool exit conditions are observed, (a) tool pushing into theworkpiece, (b) tool moving along an edge, and (c) tool encountering an adjacent edge, asshown in Figure 1. The figure shows the top view of the tool moving on the machinedsurface. Corresponding methods have been developed for each condition [2, 3]. Forexample, tool exits occur only at one point when the tool enters the workpiece along acircular arc with the tool radius centered at the point p (see Figure 2a). After entering theworkpiece, the tool starts removing the workpiece material along an edge. Down milling

will not cause tool exits in this case, as shown in Figure 2(b).

WPWP

(a) (b) (c)

p

tool

tool exit tool exit

tool

WP

tool exit

Figure 1. Three tool exit conditions: (a) Tool enters the workpiece along a straight line.

(b) Tool moves along an edge. (c) Tool encounters an adjacent edge.

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This paper presents the results of the tool path planning in conjunction with Exit OrderSequence (EOS) tools. Motivation of this study is to use both simultaneously in order to

minimize burr formation for oil filter flange and motor flange on an engine block fromthe prouction line at DaimlerChrysler, Germany. Using the tool path planning scheme,

most tool exit regions can be avoided, specially for high priority regions. However, ingeneral, it is not possible to avoid tool exit completely. For these regions, burr size can be

minimized using EOS tools. As predicted by EOS theory, burr formation dependsprimarily on tool geometry and cutting parameters [4], therefore by selecting a suitable

set of tool geometry and cutting parameters, burr size can be reduced to some extent.Sometimes, due to shoulder, fixturing and other machining constraints, tool path cannot

be modified to avoid tool exit, in this case, EOS can minimize the burr size.

p

(a) (b)

WPWP

Figure 2. (a) Tool enters a workpiece along a circular arc without tool exits.

(b) Down milling does not cause tool exits.

2. Experimental Setup

Figure 3 shows the first workpiece, an oil filter flange. Priorities can be used todistinguish regions that are critical in terms of burr formation because of functionalimportance, safety issues and/or stringent tolerance specifications. The pocket shownwith priority marked very high transports high-pressure oil, so burr formation has to beavoided at all costs. The pockets marked with priorities high carry water at high pressureand burr formation has to be minimized. For the other profiles minimizing burr formation

is an advantage but not a necessity. A typical part machined by a high speed Ex-Cell-Omachine is shown in Figure 3(b).

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(a) (b)

Figure 3. (a) Sketch of Oil filter flange showing priorities and (b) real part.

3. Experimental Results

Figure 4a shows a conventional tool path, which produces a very large tool exit area. To

avoid tool exit area in high priority regions, tool path shown in Figure 4b was developedand a tool inserts with 0

oaxial rake angle and 0

oradial rake angle or (0, 0) were used for

machining. Even with this tool path, there were still few tool exit areas in high priorityregions, which was unacceptable. To avoid these tool exit areas, a new tool path was

developed, Figure 4c. Only a very small region resulted in tool exit. For these tool exitregions, burr formation was minimized using EOS tools. A new tool (6, -6) was used for

the new tool path as it produces small burr size as predicted by EOS scheme. Figure 5shows the comparison of burr size in some exit regions. Comparison of cutting conditions

and results for different tool paths can be seen in Table 1. Though machining timeincreased slightly, it was in the acceptable range.

(a)

(b)

(a) (b) (c)

Figure 4. (a) Conventional tool path, (b) Optimized (Old) and (c) Optimized (New).

Very High

High

Medium

Low

Very High

High

Medium

Low

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Figure 5. Burr Size comparison based on tool paths (b) and (c) in Figure 4.

Milling parameters Conventional OptimizedOld

Optimized

New

Milling Cutter 40 40 40Number of inserts 3 3 3

Cutting Speed m/min 1508 2000 2000Rotation Speed rev/min 12000 15924 15924Feed per Revolution [f] 0.12 0.15 0,15

Feed Rate [mm/min] 4320 7165.8 7165,8Length of Machining [mm] 355.0 555.7 622.7

Total machining time [s] 4.9 4.7 5.3

Table 1. Comparison of cutting conditions for different tool paths for oil filter flange.

The same scheme was applied to the motor flange, shown in figure 6a. Figure 6b showsthe conventional tool path. A fixturing constraint exists for this part on the left side (not

shown in the figure). A tool path was proposed earlier (Figure 7a) to avoid exit burrcompletely. However, this tool path does not take the fixturing constraint into account.

For this area, tool exit is unavoidable. And also, the total machining time was far fromthe acceptable range due to its complexity. To reduce the processing time and avoid

workpiece constraint and exit burr in high priority region, a new tool path was developed

as shown in Figure 7b. EOS prediction scheme was used for reducing burr size in fixtureconstrained region. As predicted by EOS scheme, a low in-plane exit angle was used forthis region (tool center away from the workpiece). Again (6, -6) tool was used for burr

size reduction in exit regions. A comparison of the burr size for tool (0, 0) and (6, -6) isshown in Figure 8. An encouraging 87% reduction in burr height was achieved with the

new tool. Comparison of cutting conditions and results for different tool paths can beseen in Table 2. Machining time was reduced significantly and was with in acceptable

range.

OLD NEW

OLD

NEWOLD NEW

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(a) (b)

Figure 6. (a) Motor flange workpiece. (b) Conventional tool path.

(a) (b)

Figure 7. (a) Optimized tool (proposed earlier). (b) Newly optimized.

Figure 8. Comparison between burr size as produced by tools (0, 0) and (6, -6).

=0, =0, CBA

=6, = 6, BAC

87% reduction in average Burr Height

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Milling parameters Conventional OptimizedOld

Optimized

New

Milling Cutter 100 40 40Number of teeth 7 3 3

Cutting Speed m/min 2198 1884 1884Rotation Speed U/min 7000 15000 15000Feed per Revolution [f] 0.1 0.15 0.15Feed Rate [mm/min] 4900 6750 6750Length of Machining [mm] 222.7 888.2 434.9Total machining time [s] 2.72 7.8 3.87

Table 2. Comparison of cutting conditions for different tool paths for motor flange.

References

[1] Chu, C.H. and Dornfeld, D.A., Tool Path Planning for Exit Burr Minimization by

Estimating the Total Length of Primary Burrs, submitted for publication,International Journal of Computer Integrated Manufacturing, 1999.

[2] Chu, C.H. and Dornfeld, D.A., Tool Path Planning for Avoiding Exit Burr,submitted for publication, Journal of Manufacturing Processes, 1999.

[3] Rangarajan, A., Priority Based Tool Path Planning for Face Milling, MastersDissertation, UC Berkeley, 2001.

[4] Kumar, S., Basic Approach to Face Milling Burr Formation and Prediction System,Masters Dissertation, UC Berkeley, 2001.

[5] Bansal, A. and Lee, K., Study of Burr Size and Surface Roughness in High SpeedFace Milling, LMA reports 2002.

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Influence of Work Hardening Effect on the Exit Burr Formation

in Two Consecutive Orthogonal Cuttings

Jihong ChoiSponsored by CODEF

AbstractIn an orthogonal cutting which consist of two consecutivecutting process, the work-hardening effect induced by the first process is

expected to have an influence on the final burr formation. By finiteelement simulation and experiment, the optimal depth of cut of the first

cut for a minimal final exit burr size will be obtained.

Keywords:exit burr formation, finite element method, orthogonal cutting.

1. Prior research and background

Park [1] divided the burr formation process in orthogonal cutting largely into four stages:

initiation, initial development, pivoting point, and final development. First, beforeinitiation of pivoting point, there is no plastically deformed region in the exit boundary

(Figure 1(a)). A plastically deformed region appears on the unconstrained boundary C-Dand expands in the initiation and initial development stages (Figure 1(b)). In the pivoting

point stage, the point where material instability on the unconstrained boundary C-Doccurs due to necking phenomenon (Figure 1(c)). Work hardening effect is expected due

to this necking phenomenon in the pivoting point region. In an orthogonal cutting processconsisting of several consecutive cuts, this work hardening effect of the pivoting point

region in the formal cutting process is expected to have some influence to the burrformation in the next cutting process. The objective of this study is to investigate the

relation between the first cutting-induced work-hardening effect and final burr formationand then estimate the optimal first depth of cut in the two consecutive orthogonal cutting

process to minimize the final exit burr size.

2. Experiment and Finite Element Analysis of Orthogonal Cutting

Four different cases will be tested by experiment and finite element simulation: (i) tool

passes through the work hardened region in the second cutting process (Figure 2(a)), (ii)

tool passes through the border of the work-hardened region (Figure 2(b)), (iii) tool passesthrough the depth of cut far below the work hardened region (Figure 2(c)), and (iv)second depth of cut is set as same to the burr thickness. (Figure 2(d)). To estimate the

work-hardened region, finite element simulation will be used. To measure the machininginduced residual stresses and work hardening effect on the workpiece by experimental

method is very difficult and expensive. Finite element simulation can be an adequatemethod to investigate the remaining surface residual stresses and work-hardened region

near a burr. In this study, quantitative data will be obtained from experiment and a finite

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element analysis will be used for the qualitative investigation of work-hardening regionand for experiment design. First, through the finite element simulation, the work-

hardening region can be estimated. Based on this finite element analysis result,orthogonal cutting of various depths of cut will be tested.

Figure 1. Burr formation process in orthogonal cutting (finite element simulation) [1].

(a) Before initiation ofivoting point

(b) Initiation and

development of plastic

(c) Plastic deformation at

the pivoting point

(a)Case 1: 2ndcutting through the exitsurface work-hardened region

(d) Case 4: 2nd DOC = burr thickness

(b)Case 2: 2ndcutting through theborder of exit surface work-

hardened region

(c) Case 3: 2nd cutting through farbelow the work-hardened region

Figure 2. Three different cases in the two consecutive orthogonal cutting.

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3. Future work

The influence of work-hardening on the burr formation in practical machining processeslike milling or drilling can be investigated based on this study. Also, based on cuttingcriteria from the finite element modeling of this study, FE models can be developed for

various complicated cutting processes, like milling or drilling. Work-hardening is closelylinked to the material properties which vary with temperature and strain rate. In mostcases these material properties are unknown and it usually becomes the limitation offinite element simulation of burr formation. Material property estimation techniques forfinite element analysis should be developed for more accurate simulation.

References

[1] Park,I.,1996,Modeling of Burr Formation Processes in Metal Cutting, Ph.D.thesis, University of California, Berkeley

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Research Reports 2001-2002 39

Optimization and Quality Control in Burr Formation using Design of

Experiment (I):

Face-Milling

Kiha LeeSponsored by CODEF

AbstractThis paper presents a new method to minimize burr formationand to improve surface finish simultaneously in face-milling. The designof experiments (DOE) methods discussed in the paper can optimizecutting parameters in an efficient and reliable fashion. Four cuttingvariables used at Daimler-Chrysler were optimized through iterativeDOEs. 48(%) of burr thickness, 53(%) of burr height and 39(%) of surfaceroughness were reduced by the final optimal condition obtained from DOEII.

Keywords: burr, design of experiment, face milling.

1. Introduction

Machining is one of the most common manufacturing processes used in industry.However, burrs often occur along the workpiece edges during machining. The existenceof burrs may reduce the fit and ease of assembly, jeopardize the safety of workers duringhandling, or cause product malfunction in operation. Hence, burrs must be removed.Traditionally, a second finishing operation, known as deburring, is often used to assure

that the edges produced meet tolerance specifications. There are substantial costsassociated with the deburring operation. In addition, since deburring is difficult toautomate, it may become a bottleneck in a production line. Therefore, it is desired toavoid, or at least minimize, burr formation during machining operations.

Inputs Machine

Internal items

Output

WorkpieceToolsMotionsControlFluids

ForcesEnergyTemperaturesWearStationary zonesVibration

Pats/costParts/timeRequired geometrySurface integrity

Table 1. Inputs, outputs, and internal items in the machining system when metals are cut

[Shaw 1968].

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Although a large amount of previous research has been carried out, no generallyacceptable analytical or empirical equations for the prediction and control of burr

formation are currently available. Development of reliable analytical models forprediction and control of burr formation should be one of the main goals of burr

research. However, this is a complicated phenomenon affected by many parameters,

some of which are listed in Table 1. Hence, a trial and error method is widely used inindustry. Without an organized and systematic approach to experimentation, a costly andtime-consuming random walk approach to looking for effects of change can lead to

little or nothing in terms of enhanced knowledge of the process. The design ofexperiments (DOE) methods discussed in the paper can optimize cutting parameters in an

efficient and reliable fashion. Using the DOE method, cutting parameters were optimizedto minimize burr formation and improve surface finish simultaneously in face-milling.

2. Experimental setup and results

As shown in Figure 1(a), the DOE, based on the factorial experiment, dividesindependent variables into controllable factors and noise factors. Controllable factors are

those that can be maintained to a desired value, while noise factors are those that may notbe controlled. In the study, noise factors are machine internal items shown in Table 1.

Controllable factors have been chosen as tool geometry, speed, feed and depth of cut(DOC), as shown in Table 2. Tool geometry is divided into two variables, axial rake

angle (a) and radial rake angle (b). This experiment has been conducted at Daimler-

Chrysler in Germany. They restrict speed, feed and DOC within an acceptable range tosatisfy productivity, tool life, etc. Level 1 is chosen as a minimum value among the

range, level 3 is the maximum and level 2 is average between Levels 1 and 3. The qualityof machined part is determined by burr size (height and thickness) and surface roughness

(RMS).

System Face milling

process

Output Quality of

machined part

Noise

factors

Controllable

factors

Machine

internal items

Cutting

parameters

System Face milling

process

Output Quality of

machined part

Noise

factors

Controllable

factors

Machine

internal items

Cutting

parameters

(a) (b)

Figure 1 Definition of a system in the DOE (a) and face milling process (b).

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0.140.110.08Feed [mm/rev]

1.51.10.7DOC [mm]

20,00015,00010,000Speed [rpm]

6,-66,60,0Tool [a,b]

Level 3Level 2Level 1Controllable variables

0.140.110.08Feed [mm/rev]

1.51.10.7DOC [mm]

20,00015,00010,000Speed [rpm]

6,-66,60,0Tool [a,b]


Table 2. Controllable variables and levels of cutting conditions in DOE I.

Using a full factorial design method, given four factors and three levels, the totalexperimental run number would be 34=81. As more factors are added, the number ofexperiments increases exponentially. It takes time to conduct experiments and to measureburr size and surface roughness. Using the fractional factorial method, the total numberof runs can be reduced to minimize time invested and cost of experimentation. In thestudy, the number of experiments is reduced from 81 to 9, using an orthogonal array, L9(3)4, as seen in Table 3. Based on the array, the cutting conditions of each experimentwere set as shown in Table 4. For every cutting condition, burr height, thickness andsurface roughness were measured.

Radial Rake Angle,b

Axial Rake Angle,a

Lead Angle, g

Figure 2. Face-milling cutter with inserts, showing principal angles.

3

2

1

3

2

1

3

2

1

B

2

1

3

1

3

2

3

2

1

C

125

324

237

226

338

313

139

212

111

DARun #

3

2

1

3

2

1

3

2

1

B

2

1

3

1

3

2

3

2

1

C

125

324

237

226

338

313

139

212

111

DARun #

Table 3.Orthogonal array, L9(3)4.

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2.66

4.60

5.50

6.05

7.44

4.55

8.90

3.31

4.73

347

202

166

243

318

141

125

167

146

354

126

125

136

246

66

114

113

143

0.7

1.1

1.5

1.5

0.7

1.1

1.1

1.5

0.7

0.08

0.11

0.14

0.11

0.14

0.08

0.14

0.08

0.11

10,000

15,000

20,000

10,000

15,000

20,000

10,000

15,000

20,000

0,0

0,0

0,0

6,6

6,6

6,6

6,-6

6,-6

6,-6

1

2

3

4

5

6

7

8

9

5.30206158Average

ResultsCutting conditions

s.roughness

mm

b.height

mm

b.thickness

mm

DOC

mm

Feed

mm/rev

Speed

rpm

Tool

a,b

Run #

2.66

4.60

5.50

6.05

7.44

4.55

8.90

3.31

4.73

347

202

166

243

318

141

125

167

146

354

126

125

136

246

66

114

113

143

0.7

1.1

1.5

1.5

0.7

1.1

1.1

1.5

0.7

0.08

0.11

0.14

0.11

0.14

0.08

0.14

0.08

0.11

10,000

15,000

20,000

10,000

15,000

20,000

10,000

15,000

20,000

0,0

0,0

0,0

6,6

6,6

6,6

6,-6

6,-6

6,-6

1

2

3

4

5

6

7

8

9

5.30206158Average

ResultsCutting conditions

s.roughness

mm

b.height

mm

b.thickness

mm

DOC

mm

Feed

mm/rev

Speed

rpm

Tool

a,b

Run #

Table 4. Cutting conditions and results from DOE.

Based on the results of Table 4, response graphs were drawn (Figure 3). The squaresrepresent Design of Experiment I. Each response of controllable factors can be obtained

by averaging the corresponding experimental data. For example, the average burr-thickness response of speed for 10000 can be obtained by averaging the burr thickness of

run 1, run 4 and run 7 as follows:

2013

114136354=

++

Two important tasks can be achieved from the response graph. First, we can distinguish

the parameters that have either strong or weak effects on burr formation and surfacefinish. A high gradient in the graph means that the factor has a strong effect on theresponse. For instance, in Figure 3(a), depth of cut has a strong effect on burr thickness,

and feed has a weak effect. Second, the graph allows us to set optimal values. Forexample, speed should be set at 20000 rpm to minimize burr thickness (Figure 3(a)). To

minimize burr formation and improve surface finish simultaneously, an integratedperformance index was derived. This is a cumulative normalized index. For example, the

index of run #1 in Table 4 can be obtained as

42.430.5

66.2

206

347

158

354=++

In terms of an integrated index (Figure 3(d)), optimal cutting conditions can bedetermined to minimize burr formation and improve surface finish in DOE I. The optimal

conditions are (6, -6) degrees, 20000 rpm, 0.11 mm/rev and 1.1 mm for rake angles,spindle speed, feed rate and DOC, respectively. Since speed, feed and DOC are not

discrete values in reality, it is possible to optimize the values and improve the quality ofthe machined part. For this reason, a second DOE was performed, based on the optimal

values obtained from DOE I. Table 5 shows the controllable variables and the levels of

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cutting conditions in DOE II. Following the same procedure as in DOE I, DOE II wasimplemented. The response graph is shown in Figure 3 as circles. Final optimal

conditions are (6, -6) degrees, 20000 rpm, 0.08 mm/rev and 1.1 mm for rake angle,spindle speed, feed, and DOC, respectively. Compared to DOE I, average values of burr

thickness, burr height and surface roughness were reduced 48 %, 53 % and 39 %,

respectively. The difference in burr sizes can be seen in Figure 4.

(a) average burr thickness (b) average burr height

(c) surface roughness (d) integrated performance index

Figure 3. Response graphs from DOE I and DOE II.

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Figure 4. Typical burr formation (left) and burr formation (right) using the optimal

cutting condition derived from DOE II.

0.120.10.08Feed [mm/rev]

1.51.31.1DOC [mm]

23,00020,00017,000Speed [rpm]

6,-6Tool [a,b]


0.120.10.08Feed [mm/rev]

1.51.31.1DOC [mm]

23,00020,00017,000Speed [rpm]

6,-6Tool [a,b]


Table 5. Controllable variables and levels of cutting conditions in DOE II.

References

[1] Shaw, M. C.,Metal Cutting Principles, Clarendon Press, Oxford, England, 1984.

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Optimization and Quality Control in Burr Formation u1sing Design of

Experiment (II):

Drilling of Intersecting Holes

Kiha LeeSponsored by CODEF

AbstractThis paper presents a method to minimize burr formation indrilling intersecting holes using Design of Experiments (DOE). Threecontrollable parameters: tool geometry, speed and feed, were optimizedthrough iterative DOEs. An 88 % reduction in burr size was obtainedusing the final optimal conditions from DOE II.

Keywords: burr formation, design of experiment, drilling, drill geometry.

1. Introduction

The drilling process of the crankshaft of automobile engines (Figure 1) motivated this

study. Through holes, which go through the crankshaft perpendicularly, are drilled first.Afterwards, holes are drilled to intersect the through holes. Drilling exit burrs are formed

around the periphery of the intersecting holes and additional deburring processes areneeded to remove the burrs.

Figure 1. Workpiece showing intersecting hole.

Burr minimization in drilling of intersecting holes is especially important, because it

usually allows a very limited access for deburring tools. Even though it is a very common

practice in industry, very little research has been carried out on the burr formationmechanism in intersecting hole drilling. Stein [1] and Kim [2], conducted acomprehensive experimental study in this area.

Figure 2 is the proposed sequence of events of burr formation during intersecting hole

drilling, based on observations in this study. Since the exit angle (f) varies with the

angular location around the intersecting drilled hole, the stiffness of the material under

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the drill also varies. The point of minimum exit angle has the smallest stiffness and thematerial under the point will easily be bent and pushed out to form the largest burr.

The objective of the study is to minimize burr formation in intersecting holes usingDesign of Experiments (DOE). Lee [3] proposed a method to minimize burr formation

and to improve surface finish in face milling simultaneously using DOE. A similar

approach has been applied to minimize burr formation in intersecting holes. Using themethod, dominant variables which have a strong effect on burr formation have beendistinguished. In addition, optimal cutting conditions have been determined and the

process tuned.

Figure 2. Burr formation in intersecting hole drilling.

2. Experimental setup and results

The experiments were carried out at Daimler-Chrysler in Germany. The material is20MoCr4, which is similar to AISI 4118. A picture of the workpiece is shown in Figure

1. Experimental variables: tool geometry, speed and feed, are shown in Figure 3. For tool

geometry, level 1 is a conventional drill. Level 2 is a 20additional chamfer drill withsplit point and level 3 is a 40additional chamfer drill.

0.120.080.04Feed [mm/rev]

1,6001,3001,000Speed [rpm]

Double chamferDouble chamfer

with split point

Conventional

Tool

Level 3Level 2Level 1Controllable

variables

0.120.080.04Feed [mm/rev]

1,6001,3001,000Speed [rpm]

Double chamferDouble chamfer

with split point

Conventional

Tool

Level 3Level 2Level 1Controllable

variables

Figure 3.Controllable variables and their levels.

flarge burr

small burr

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Figure 4. Response graph of DOE I (a) and DOE II (b).

Figure 5. Typical burr formation (left) and burr formation (right) using the optimalcutting conditions obtained from DOE II.

References

[1] J. Stein, I. Park, D. A. Dornfeld, Influence of Workpiece Exit Angle on BurrFormation in Drilling Intersecting Holes, Trans. North American ManufacturingResearch Institute, SME, vol. 24, 1996, pp. 39-44.

[2] J. Kim, D. A. Dornfeld, R. J. Furness, Experimental Study of Burr Formation inDrilling of Intersecting Holes with Gun and Twist Drills, Tech. Papers,NAMRI/SME, 1999, pp. 39-44.

[3] K. Lee, Optimization and Quality Control in Burr Formation Using Design ofExperiment (I): Face-Milling, 2001 LMA Annual Reports.

(a) (b)

a (b)

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A Study of Surface Roughness in Micro-End-Milling of Aluminum

Kiha LeeSponsored by CODEF and ACLARA

Abstract A series of experiments has been conducted in order tocharacterize the factors that affect surface roughness, and to determine the

range of attainable surface roughness values for the micro-end-millingprocess. A 229mm diameter end mill was used to cut slots into aluminium

6061 samples. The machining factors studied were chip load (feed pertooth), cutting speed, and depth of cut.

Keywords: aluminum, micro end milling, surface roughness.

1. Introduction

The end-milling process is one of the most widely used material removal processes inindustry. In recent years, the micro-end-milling process has received increased attention.Micro-end-milling refers to a basic end milling process were tools down to 50 mm indiameter are used. Micro-end-milling is essentially the same process as end-milling onthe macro scale. However, there are a few important differences. As the tool diameterbecomes smaller, the rotational speed theoretically required to achieve the recommendedcutting speed is far above the technical limit of the available spindles. Another concern inmicro-milling is that run-out can become comparable to the diameter of the tools used.The run-out to tool diameter ratio is much larger for micro-end-milling than fortraditional milling.

2. Experimental setup

A Mori Seiki CNC drilling center was used for the experiments. The drilling center has a

maximum spindle speed of 8000 rpm. However, by using an Air Turbine tool attachment,operation at 40000 rpm is allowed. A 229 mm diameter tool from Robbjack Corporation

was used for the experiments. The end-mills are made of 92% WC and 8% Co. Surfaceroughness measurements were taken with a diamond stylus (Tenco P-10), traveling along

a straight line over the surface of the machined workpiece. It features the ability to

measure micro-roughness with up to 0.5 resolution.

3. Results and Analysis

A two level factorial analysis was performed on a subset of the data. A graphical

representation of the results is shown in Figure 1. The high and low values for each of theparameters, along with the calculated effect of each parameter is also shown in Figure 1.

The high and low values for depth of cut correspond to 1/2 and 1/4 the tool diameter,

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respectively. The cutting speed values correspond to 7500 rpm and 40000 rpm with a 229mm diameter tool.

ChipLoad

Depth of Cut

653 676

610

2529

3843

652

1725

3596

Cutting

Speed

Parameter High LowChip Load (ft) 5.7 mm 0.83 mm

Cutting Speed (vc) 0.48 m/s 0.089 m/s

Depth of Cut (aa) 114 mm 57 mm

Parameter Effect ()ft 2276

vc -813

aa 258

ft*vc -779.5

ft*aa 267.5

vc*aa 123

ft*vc*aa 155.5

Figure 1. Graphical representation of the results of the 2 level factorial analysis.

The numbers at the corners of the cube are the surface roughness values in Angstroms.The effects of each parameter and interactions are also shown.

The chip load is by far the most dominant factor affecting the surface roughness.However, cutting speed and cutting speed chip load interaction are also significant, as

shown on the normal probability plot (Figure 2). In this plot, any point that lies off astraight line fit can be considered a real effect and not due to random variations in the

process. The effects of the other factors (points not labeled in Figure 2) are within thenoise of the process and have been left out of the models developed.

Although cutting speed and the cutting speed chip load interaction are significant, their

effect is far smaller than the effect of chip load. A linear model was developed using the

data from the 2 level factorial analysis:

ctcta vfvfR 8185896975.96 -++=

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Figure 2. Normal probability plot of main effects and interactions. Points that lie off a

straight line fit (labeled) represent the significant effects.

Four different chip load levels were tested in order to generate a quadratic model for this

factor. The extra two levels were added between the high and low levels used for thetwo-level factorial. Surface roughness results for two different depths of cut are shown in

Figure 3.

Figure 3. Surface Roughness as a function of chip load for 2 different depths of cut andcutting speeds. The trend line is the value predicted by a quadratic polynomial model.

The relationship between chip load and surface roughness appears slightly non-linear,particularly at high cutting speed. A polynomial model was developed incorporating asecond order term for chip load.

ctctta vfvffR 99012563.464396.43 2

-+++=

d=229um, DOC = D/2

0

500

1000

1500

2000

2500

30003500

4000

4500

0 2 4 6Chip Load (um)

Ra(Angstroms)

Vc = 0.09 m/s

Vc = 0.48 m/s

d=229 um, DOC = D/4

0

500

1000

1500

2000

2500

30003500

4000

4500

0 2 4 6Chip Load (um)

Ra(Angstroms

)Vc = 0.09 m/s

Vc = 0.48 m/s

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Figure 4 shows the surface roughness traces for several slots, machined with differentcombinations of chip load and cutting speed. SEM micrographs of the machined slots are

also shown. Large marks from the cutting tooth (a deep valley followed by a high peak)are easily visible on the surface roughness traces. These marks can also be seen in the

adjoining SEM micrographs. Interestingly, the period from large peak to large peak is

twice the chip load, which means that the large marks are created once per revolutionrather than once for each tooth. In many of the surface roughness traces, a step is clearlyvisible midway between larger peaks. This phenomenon is most likely the result of run-

out.

A functional part that will be used in a meso-scale electricity conversion system, forpotential use in very low power electronics, was fabricated as a verification part (Figure

5). The entire size of the part is 17.5 mm x 17.5 mm x 0.5 mm.

-500

-400

-300

-200

-100

0

100

200

300

400

500

140 150 160 170 180

Feed distance [um]

Surfaceroughness[nm]

Figure 4. Surface Roughness Traces and SEM Micrographs at 7500 rpm speed, 1/2dDOC, and 4.233um/tooth feed for low and high chip loads.

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Up-millingDown-milling

Figure 5. Photograph of verification part and SEM micrograph of the verification part.

One arm broke during processing. The picture on the right shows a comparison ofsurface roughness for up-milling and down-milling.

Although the surface roughness was not measured for this part, SEM micrographs weretaken and show some interesting qualitative results. Figure 5 shows a micrograph of theverification part. The micrograph on the right shows the surface of the large pocket

milled out of the center. Both up-milling and down-milling were used on this portion ofthe pocket as shown in Figure 5. From the micrograph, it can be seen that the surface

roughness is better for down-milling, as is commonly the case.

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Integrated Tool Path Planning and Burr Prediction System for Face

Milling

Prabhu Ramachandran and Amit BansalSponsored by CODEF

AbstractIn recent years there has been significant progress in the fieldsof tool path planning and burr prediction systems for the face millingoperation. The objective of this research is to bring priority based tool pathplanning and Exit Order Sequence to a common integrated platform. Theoptimal tool path generated by the scheme is the result of interactionbetween a theoretical approach and the knowledge base gained byexperimentation.

Keywords:burr prediction, exit order sequence, face milling, integrated

user interface, tool path planning.

1. Introduction

Priority based tool path planning[1, 2] is a practical tool-path planning scheme for exitburr minimization, based on assignment of priorities to various features depending on theedge quality desired. Priorities can be used to distinguish regions that are critical in termsof burr formation because of functional importance, safety issues and/or stringenttolerance specifications.

Exit Order Sequence (EOS)[3] is defined in terms of three points (A, B and C) on thetool insert as shown in Figure 1: A on the minor cutting edge (exact location in 3D isdetermined by feed value and direction), C on the major cutting edge (exact location in3D is determined on unmachined surface) and B at the intersection of the two edges (atthe depth of cut). The sequence in which these points come out or exit of the work pieceedge determines the EOS and burr size. Six different EOS are possible ABC, BAC, ACB,BCA, CAB, and CBA in the order of increasing burr size.

Figure 1. Exit Order Sequence.

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Algorithms for these two tools have been developed in the laboratory for manufacturingautomation. Currently efforts have been made on integration of these two tools on acommon platform with an integrated Graphical User Interface to make the processplanning more user-friendly.

2. Architecture for integrated platform

The new platform has been developed for the windows operating system for easy userinterface and interaction. In the proposed architecture the software that runs on VisualC++ using the ACIS Geometric Modeler reads the part file in the sat format.

2.1. Tool-Path planning

User input is used to prioritize various regions and to identify shoulders and fixturing

constraints. A bounding region for the tool path that minimizes the high exit and high

entrance angle conditions is generated based on the information obtained. The shortesttool path within the previously generated bounding region is chosen as the initial toolpath to minimize the machining time. Recommendations are made for the selection of

tool diameter if a single pass operation is desired based on the tool swept area. Apreliminary attempt is made to develop a tool path.

2.2.Burr prediction

The exit order prediction scheme is then applied to obtain the EOS for the current toolpath. The EOS provides useful information on regions which have undesirable burrformation. For example EOS of BCA, CAB and CBA lead to large burrs. Information

regarding various EOSs present is stored in a data structure with the location of burr onthe work piece edges.

2.3.Integration

This information is then transferred back to the tool path planner. Local modifications aremade to the tool path to remove segments that have an undesirable EOS. The loopbetween the tool path planner and the burr prediction system is traversed repeatedly untilthe desired burr minimization objective is achieved. The NC code is generated for theoptimal tool path that results from the previous algorithm.

The burr size distribution corresponding to the various exit order sequences present in theoptimal tool path is generated using a database connectivity tool. The database used forthis purpose has been the result of experiments conducted both at the University ofCalifornia, Berkeley and at various industrial organizations. The database provides amean value as well as an interval where the value is most likely to fall.

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Figure 2 shows the block diagram of the steps involved within the software tool. Figure 3shows a snapshot of the output of the software with a simple extruded part and theoptimal tool path.

Figure 2. Steps involved in the integrated user interface.

Figure 3. Snapshot of the output with a simple extruded part and its optimal tool path.

Tool PathPlanner

ComprehensiveEOS Scheme

Burr SizeDatabase

Feedback

CADfile

Integrated User Interface

Optimized tool path withburr locations

EOS shown with theoptimized tool pathand burr size

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References

[1] Rangarajan, A and Chu, C.H. and Dornfeld, D.A., Avoiding Tool Exit in PlanarMilling by Adjusting Width of Cut, Proceedings of the ASME,ManufacturingEngineering Division, ED, Vol. 11, pp. 1017-1027, 2000.

[2] Rangarajan, A., Priority Based Tool Path Planning for Face Milling, MastersDissertation 2001, UC Berkeley.

[3] Kumar, S., Basic Model for Burr Prediction System, Masters Dissertation 2001,UC Berkeley.

[4] Narayanaswami, R. and Dornfeld, D.A., Burr Minimization in face milling ageometric approach, ASME Journal Of Manufacturing Science and Engineering,Vol. 119, May 1997, pp. 170-177.

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Probabilistic based Precision Process Planning P4

Arvind RangarajanSponsored by CODEF

Abstract Progress and innovation are necessary to sustain marketleadership for automotive companies. Advanced manufacturingengineering is a core competency required to make quality automobiles.Complete understanding of each of the manufacturing processes used inthe production of the automobile is important. This report describes aframework that helps identify and preserve those core competencies,suggest avenues to expand those in the future to maintain marketleadership, and to clearly identify the stages where innovation orsignificant improvement is immediately necessary. P4 is a scientificapproach for aggressive manufacturing.

Keywords: probabilistic, process planning.

1. Problem Description

The experiences gained during our collaboration with industry over several years haveidentified some of the limitations faced by engineers in production. This proposaloutlines a solution strategy to eliminate the following shortcomings:

1. Processes that are widely used for a particular material class are being optimized toofar into production. This results in temporary unstable solutions. This is often due to

process planning being done elsewhere (e.g. machine tool manufacturers) and lack ofsoftware tools to check for optimization of the designed process plan beforeproduction.

2. Sensitivity of the manufacturing process to the inherent variability in the system isoften high. For example, in face milling, the process should be operating in a regionwhere there is little or no sensitivity to silicon content of the alloy, coolant

composition, etc. Tools to assist in determining these stable regions would behelpful.

3. Process balancing could be improved and the processes are often not optimized as

part of the sequence generating the desired precision dimensions. The process wouldbenefit from more of a systematic approach.

4. Information about processes that are well-understood and successful (trouble free) isoften not transferred to the design engineer to aid concurrent engineering. Tools to

facilitate this transfer of information and its use as part of the design process wouldbe helpful.

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2. Solution Framework

The solution framework is a systematic procedure that would be accompanied by a suiteof software tools that would aid plant, process and design engineers to alleviate theproblems described above. Even though the framework is universal the focus of our work

is on machining processes, the implementation would be limited to understanding andoptimizing a sequence of machining steps. The following tasks have to be accomplishedfor creating such a system:

a) Identify the weak and strong process links in the process chain or sequence usingPareto analysis. Here weak refers to a condition when the influence of parameters and

its position in sequence is not well established.

b) Estimate the level of understanding at the company for individual processes andsuggest experimental plans to fill in missing details. The process domain can be

explored by using probability of achieving the specification as the desired

quantitative measure.

c) Develop experimental analysis tools to extract useful and relevant information tooptimize each process and, at the same time, generate the desired probabilityinformation.

d) Divide the feasible domain into stable and unstable regions with analytical andempirical data.

e) Understand the coupling be

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